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Weige Zhang
School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China

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Journal article
Published: 09 July 2021 in Energies
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To relieve the peak operating power of the electric grid for an electric bus fast-charging station, this paper proposes to install a stationary energy storage system and introduces an optimization problem for obtaining the optimal sizes of an energy buffer. The charging power demands of the fast-charging station are uncertain due to arrival time of the electric bus and returned state of charge of the onboard energy storage system can be affected by actual traffic conditions, ambient temperature and other factors. The introduced optimization is formulated as a stochastic program, where the power matching equality of the total charging demands of connected electric buses is described as a chance constraint by denoting a satisfaction probability, then a stochastic supremum for the operating power of the electric grid is defined by actual data and the problem finally can be solved by convex programming. A case study for an existing electric bus fast-charging station in Beijing, China was utilized to verify the optimization method. The result shows that the operation capacity cost and electricity cost of the electric grid can be decreased significantly by installing a 325 kWh energy storage system in the case of a 99% satisfaction probability.

ACS Style

Xiaowei Ding; Weige Zhang; Shaoyuan Wei; Zhenpo Wang. Optimization of an Energy Storage System for Electric Bus Fast-Charging Station. Energies 2021, 14, 4143 .

AMA Style

Xiaowei Ding, Weige Zhang, Shaoyuan Wei, Zhenpo Wang. Optimization of an Energy Storage System for Electric Bus Fast-Charging Station. Energies. 2021; 14 (14):4143.

Chicago/Turabian Style

Xiaowei Ding; Weige Zhang; Shaoyuan Wei; Zhenpo Wang. 2021. "Optimization of an Energy Storage System for Electric Bus Fast-Charging Station." Energies 14, no. 14: 4143.

Journal article
Published: 24 February 2021 in Energies
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To enhance the operational reliability and safety of electric vehicles (EVs), big data platforms for EV supervision are rapidly developing, which makes a large quantity of battery data available for fault diagnosis. Since fault types related to lithium-ion batteries play a dominant role, a comprehensive fault diagnosis method is proposed in this paper, in pursuit of an accurate early fault diagnosis method based on voltage signals from battery cells. The proposed method for battery fault diagnosis mainly includes three parts: variational mode decomposition in the signal analysis part to separate the inconsistency of cell states, critical representative signal feature extraction by using a generalized dimensionless indicator construction formula and effective anomaly detection by sparsity-based clustering. The signal features of the majority of signal-based battery fault detection studies are found to be particular cases with a specific set of parameter values of the proposed indicator construction formula. With the sensitivity and stability balanced by appropriate moving-window size selection, the proposed signal-based method is validated to be capable of earlier anomaly detection, false-alarm reduction, and anomalous performance identification, compared with traditional approaches, based on actual pre-fault operating data from three different situations.

ACS Style

Xinwei Cong; Caiping Zhang; Jiuchun Jiang; Weige Zhang; Yan Jiang; Linjing Zhang. A Comprehensive Signal-Based Fault Diagnosis Method for Lithium-Ion Batteries in Electric Vehicles. Energies 2021, 14, 1221 .

AMA Style

Xinwei Cong, Caiping Zhang, Jiuchun Jiang, Weige Zhang, Yan Jiang, Linjing Zhang. A Comprehensive Signal-Based Fault Diagnosis Method for Lithium-Ion Batteries in Electric Vehicles. Energies. 2021; 14 (5):1221.

Chicago/Turabian Style

Xinwei Cong; Caiping Zhang; Jiuchun Jiang; Weige Zhang; Yan Jiang; Linjing Zhang. 2021. "A Comprehensive Signal-Based Fault Diagnosis Method for Lithium-Ion Batteries in Electric Vehicles." Energies 14, no. 5: 1221.

Journal article
Published: 25 January 2021 in IEEE Access
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A large proportion of electric vehicle accidents are attributed to lithium-ion battery failure recently, which demands the time-efficient diagnosis and safety warning in advance of severe fault occurrence to ensure reliable operation of electric vehicles. However, serious battery system faults are often not caused by easily-observed cell state inconsistency, but derived from a certain cell failure with precursory signals untended, or occasional abuse, thus eventually thermal runaway. In this paper, a signal-based fault diagnosis method is presented, including signal analysis to eliminate the impact of state inconsistency on time-series feature extraction, feature fusion, and dimensionality reduction by manifold learning, with clustering-based outlier detection to identify abnormal signal features. The challenges in threshold determination of fused features can be effectively resolved by supplementary correction to largely reduce the amount of false alarms. Compared with the judgments from actual battery management systems, and other signal-based methods with single features, earlier detections can be achieved with robustness, verified by real-world pre-fault operation data of electric vehicles that suffered thermal runaway.

ACS Style

Jiuchun Jiang; Xinwei Cong; Shuowei Li; Caiping Zhang; Weige Zhang; Yan Jiang. A Hybrid Signal-Based Fault Diagnosis Method for Lithium-Ion Batteries in Electric Vehicles. IEEE Access 2021, 9, 19175 -19186.

AMA Style

Jiuchun Jiang, Xinwei Cong, Shuowei Li, Caiping Zhang, Weige Zhang, Yan Jiang. A Hybrid Signal-Based Fault Diagnosis Method for Lithium-Ion Batteries in Electric Vehicles. IEEE Access. 2021; 9 ():19175-19186.

Chicago/Turabian Style

Jiuchun Jiang; Xinwei Cong; Shuowei Li; Caiping Zhang; Weige Zhang; Yan Jiang. 2021. "A Hybrid Signal-Based Fault Diagnosis Method for Lithium-Ion Batteries in Electric Vehicles." IEEE Access 9, no. : 19175-19186.

Journal article
Published: 26 November 2020 in Energies
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This paper introduces an optimal sizing method for a catenary-free tram, in which both on-board energy storage systems and charging infrastructures are considered. To quantitatively analyze the trade-off between available charging time and economic operation, a daily cost function containing a whole life-time cost of energy storage and an expense of energy supplies is formulated for the optimal sizing problem. A mixed particle swarm optimization algorithm is utilized to find optimal solutions for three schemes: (1) ultracapacitors storage systems with fast-charging at each station; (2) battery storage systems with slow-charging at starting and final stations; (3) battery storage systems with fast-swapping at swapping station . A case study on an existing catenary-free tramline in China is applied to verify the effectiveness of the proposed method. Results show that a daily-cost reduction over 30% and a weight reduction over 40% can be achieved by scheme 2, and a cost saving of 34.23% and a weight reduction of 32.46% can be obtained by scheme 3.

ACS Style

Ying Yang; Weige Zhang; Shaoyuan Wei; Zhenpo Wang. Optimal Sizing of On-Board Energy Storage Systems and Stationary Charging Infrastructures for a Catenary-Free Tram. Energies 2020, 13, 6227 .

AMA Style

Ying Yang, Weige Zhang, Shaoyuan Wei, Zhenpo Wang. Optimal Sizing of On-Board Energy Storage Systems and Stationary Charging Infrastructures for a Catenary-Free Tram. Energies. 2020; 13 (23):6227.

Chicago/Turabian Style

Ying Yang; Weige Zhang; Shaoyuan Wei; Zhenpo Wang. 2020. "Optimal Sizing of On-Board Energy Storage Systems and Stationary Charging Infrastructures for a Catenary-Free Tram." Energies 13, no. 23: 6227.

Journal article
Published: 17 November 2020 in IEEE Transactions on Intelligent Transportation Systems
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With the development of extreme fast charging technology, charging stations need to use energy storage stations to reduce the rising peak to average power ratio (PAPR). Lithium-ion capacitor (LIC) is a chemical power source that uses both Faraday process and non-Faraday process to store energy. Because of its attractive performance in terms of rate characteristics and chemical stability, it is suitable for some energy storage stations that consider both power density and energy density. It is important to describe the current-voltage characteristics of LIC to predict the charge and discharge efficiency in the early design of energy storage power stations. During the test, however, a full discharge or charge results in a high temperature rise, and the electrical model parameters near a specific temperature point cannot be accurately obtained. The short current pulses cannot stabilize the polarization. In this paper, a high-accuracy parameters identification method based on an improved Butler-Volmer-Equation-Based electrical model is used to summarize the phenomena caused by the rate of change of high-energy LIC. The accuracy of the method is tested under the dynamic stress condition test. The maximum voltage error is less than 2%. Energy efficiency calculation based on the used model is simulated by the design condition from the energy storage station of the Haizhu line in Guangzhou. The maximum error is less than 0.2%.

ACS Style

Hao Li; Jiuchun Jiang; Weige Zhang; Linjing Zhang; Ying Yang; Anci Chen; Xinyuan Fan. High-Accuracy Parameters Identification of Non-Linear Electrical Model for High-Energy Lithium-Ion Capacitor. IEEE Transactions on Intelligent Transportation Systems 2020, 22, 651 -660.

AMA Style

Hao Li, Jiuchun Jiang, Weige Zhang, Linjing Zhang, Ying Yang, Anci Chen, Xinyuan Fan. High-Accuracy Parameters Identification of Non-Linear Electrical Model for High-Energy Lithium-Ion Capacitor. IEEE Transactions on Intelligent Transportation Systems. 2020; 22 (1):651-660.

Chicago/Turabian Style

Hao Li; Jiuchun Jiang; Weige Zhang; Linjing Zhang; Ying Yang; Anci Chen; Xinyuan Fan. 2020. "High-Accuracy Parameters Identification of Non-Linear Electrical Model for High-Energy Lithium-Ion Capacitor." IEEE Transactions on Intelligent Transportation Systems 22, no. 1: 651-660.

Journal article
Published: 03 November 2020 in IEEE Transactions on Intelligent Transportation Systems
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Power simulation of lithium ion battery through battery model is of great significance for dynamic response simulation, heat generation calculation and charge-discharge strategy development. The accuracy and applicability of the model become crucial. In order to demonstrate the battery transient characteristics more effectively, a novel identification method for parameters of the 2nd order RC equivalent circuit model was proposed. Based on the derived evolution law of battery transient characteristics under the continuous pulse excitation, four feature points are extracted for parameter identification in each cycle. The proposed method reduced the time cost of identification from 11796.88s to 0.06s while ensuring that the error of voltage doesn't exceed 2.2mV. In order to verify the power profiles applicability of the proposed method, applicability analysis of power profile for different identification methods was carried out including the methods using different amount of data (4N points, 200 points, 6000 points) under unidirectional current pulse excitation (UCPE), bidirectional current pulse excitation (BCPE) and unidirectional voltage pulse excitation (UVPE). It was illustrated that the identification process using data of multiple cycles could significantly reduce errors, including maximum error and average error. What's more, the proposed method under UCPE had the lowest maximum error of 0.420% in voltage simulation and -0.421% in the current simulation of power profiles. Compared with the conventional method (using 200 points of single pulse data for parameter identification), the proposed method can reduce the average voltage error and the maximum error by 62.5% and 11.8% respectively under the DST power profile.

ACS Style

Bingxiang Sun; Xitian He; Weige Zhang; Haijun Ruan; Xiaojia Su; Jiuchun Jiang. Study of Parameters Identification Method of Li-Ion Battery Model for EV Power Profile Based on Transient Characteristics Data. IEEE Transactions on Intelligent Transportation Systems 2020, 22, 661 -672.

AMA Style

Bingxiang Sun, Xitian He, Weige Zhang, Haijun Ruan, Xiaojia Su, Jiuchun Jiang. Study of Parameters Identification Method of Li-Ion Battery Model for EV Power Profile Based on Transient Characteristics Data. IEEE Transactions on Intelligent Transportation Systems. 2020; 22 (1):661-672.

Chicago/Turabian Style

Bingxiang Sun; Xitian He; Weige Zhang; Haijun Ruan; Xiaojia Su; Jiuchun Jiang. 2020. "Study of Parameters Identification Method of Li-Ion Battery Model for EV Power Profile Based on Transient Characteristics Data." IEEE Transactions on Intelligent Transportation Systems 22, no. 1: 661-672.

Journal article
Published: 09 October 2020 in Applied Energy
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To realize economical operation of a catenary-free tramline, we propose installing a stationary energy storage system (SESS) to assist the electric grid for trams charging. As the tram operation may not be fully aligned with a predetermined timetable, an economical coordination of the electric grid and the SESS under uncertain charging demands is investigated. To this end, a chance-constrained program is formulated where demanded charging of the tramline is satisfied in a probabilistic sense. The introduced chance-constrained program is translated into a robust and deterministic mixed-integer second-order cone program (MISOCP) by first saturating charging power to a stochastic upper limit and then prolonging charging periods until entire energy is delivered for all charging scenarios that are being investigated. A case study for the Haizhu line in Guangzhou, China, shows that a cost–benefit of 13.70% can be obtained by installing an SESS when charging power is fully delivered for all scenarios, while a 28.47% cost-saving can be achieved when charging power is delivered 99% of the time.

ACS Style

Shaoyuan Wei; Nikolce Murgovski; Jiuchun Jiang; Xiaosong Hu; Weige Zhang; Caiping Zhang. Stochastic optimization of a stationary energy storage system for a catenary-free tramline. Applied Energy 2020, 280, 115711 .

AMA Style

Shaoyuan Wei, Nikolce Murgovski, Jiuchun Jiang, Xiaosong Hu, Weige Zhang, Caiping Zhang. Stochastic optimization of a stationary energy storage system for a catenary-free tramline. Applied Energy. 2020; 280 ():115711.

Chicago/Turabian Style

Shaoyuan Wei; Nikolce Murgovski; Jiuchun Jiang; Xiaosong Hu; Weige Zhang; Caiping Zhang. 2020. "Stochastic optimization of a stationary energy storage system for a catenary-free tramline." Applied Energy 280, no. : 115711.

Journal article
Published: 30 September 2020 in IEEE Transactions on Intelligent Transportation Systems
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The mechanism revelation of performance decrease and fast-charging limitation of lithium-ion batteries at low temperatures is indispensable to optimize battery design and develop fast-charging methods. In this article, an electrochemical model-based quantitative analysis method is proposed to uncover the dominant reason for performance decrease and fast-charging limitation of batteries at low temperatures. The highly important dynamic parameters are carefully determined by the experimental data from the checked three-electrode battery and optimized by the genetic algorithm, rather than directly taken from the references. Validation results confirm that the electrochemical model can well reproduce battery behaviors under different conditions and that identified parameters are accurate. The quantitative analysis indicates that the sluggish diffusion in cathode and anode electrodes is the principal reason for battery available capacity loss. Battery available power attenuation is primarily attributed to the increased film resistance of anode and the reduced exchange current density of cathode, and it is substantially independent of the reduced diffusivity. The comparison result from the lithium-plating-prevention charging current reveals that the increased film resistance of the anode is responsible for the predominant limitation of low-temperature fast-charging, despite the most change in the exchange current density of the anode. This quantitative revelation breaks through the traditional understanding from the qualitative analysis that performance decrease and fast-charging limitation of batteries at low temperatures are highly associated with the degree of the change of characteristic parameters.

ACS Style

Haijun Ruan; Bingxiang Sun; Weige Zhang; Xiaojia Su; Xitian He. Quantitative Analysis of Performance Decrease and Fast-Charging Limitation for Lithium-Ion Batteries at Low Temperature Based on the Electrochemical Model. IEEE Transactions on Intelligent Transportation Systems 2020, 22, 640 -650.

AMA Style

Haijun Ruan, Bingxiang Sun, Weige Zhang, Xiaojia Su, Xitian He. Quantitative Analysis of Performance Decrease and Fast-Charging Limitation for Lithium-Ion Batteries at Low Temperature Based on the Electrochemical Model. IEEE Transactions on Intelligent Transportation Systems. 2020; 22 (1):640-650.

Chicago/Turabian Style

Haijun Ruan; Bingxiang Sun; Weige Zhang; Xiaojia Su; Xitian He. 2020. "Quantitative Analysis of Performance Decrease and Fast-Charging Limitation for Lithium-Ion Batteries at Low Temperature Based on the Electrochemical Model." IEEE Transactions on Intelligent Transportation Systems 22, no. 1: 640-650.

Journal article
Published: 15 September 2020 in IEEE Transactions on Vehicular Technology
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A hybrid method for the prediction of the remaining useful life (RUL) of Lithium-ion batteries considering error-correction is proposed in respect of capacity diving phenomenon in later capacity degradation. First, an improved empirical capacity degradation model is proposed based on our previous work, with the analytic expression further revised in this paper to enhance its fitting accuracy, parameter identifiability, and applicability to tracking algorithm. Unscented particle filter (UPF) algorithm is then implemented to obtain prognostic results to produce original error series. Next, to enhance the quality of original error data by reducing the uncertainty, complete ensemble empirical mode decomposition (CEEMD) algorithm is utilized to reconstruct error series. The fundamental error evolution information is retained by selecting relatively highly correlated intrinsic mode functions (IMFs) of the decomposition results of original error series. Finally, after employing Gaussian process regression (GPR) algorithm, the prognostic error to correct the UPF-based prognostic results is obtained from the reconstructed error series. RUL prediction experiments for batteries with different working conditions have been conducted to verify the improved performance of the proposed model and the hybrid method, with mean absolute percentage errors of battery capacity degradation predicted less than 0.4%.

ACS Style

Xinwei Cong; Caiping Zhang; Jiuchun Jiang; Weige Zhang; Yan Jiang. A Hybrid Method for the Prediction of the Remaining Useful Life of Lithium-Ion Batteries With Accelerated Capacity Degradation. IEEE Transactions on Vehicular Technology 2020, 69, 12775 -12785.

AMA Style

Xinwei Cong, Caiping Zhang, Jiuchun Jiang, Weige Zhang, Yan Jiang. A Hybrid Method for the Prediction of the Remaining Useful Life of Lithium-Ion Batteries With Accelerated Capacity Degradation. IEEE Transactions on Vehicular Technology. 2020; 69 (11):12775-12785.

Chicago/Turabian Style

Xinwei Cong; Caiping Zhang; Jiuchun Jiang; Weige Zhang; Yan Jiang. 2020. "A Hybrid Method for the Prediction of the Remaining Useful Life of Lithium-Ion Batteries With Accelerated Capacity Degradation." IEEE Transactions on Vehicular Technology 69, no. 11: 12775-12785.

Journal article
Published: 31 July 2020 in IEEE Transactions on Intelligent Transportation Systems
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With the development of battery technology, large-scale battery applications are increasing. In order to obtain a higher current and voltage level and improve the overall energy efficiency, batteries are connected in series and parallel. Bulk model is the most used model to simulate battery packs, and the simulation results of single cell are enlarged several times to represent a battery pack. But bulk model ignores inconsistency between the batteries, and when the current magnification is large, such as in the extreme fast charge scenario, this neglect cannot reflect the current distribution inside the battery pack. Our paper proposes a simplify algorithm based on equivalent circuit model, which applied for non-uniform series and parallel-connected batteries. This simplification can greatly improve simulation speed while maintaining accuracy under the consideration of inconsistency. More importantly, this algorithm applies to any complex circuit, regardless of how many series-parallel structure layers. With the proposed algorithm, Monte Carlo simulations were evaluated to obtain the influence of battery series and parallel number and normal distribution parameters on current distribution. Simulation results show that an increase in the number of series will reduce the inconsistency of the current distribution, and an increase in the number of parallel and an increase in the relative standard deviation of the normal distribution of the battery parameters will increase the inconsistency of the current distribution.

ACS Style

Xinyuan Fan; Weige Zhang; Zhanguo Wang; Fulai An; Hao Li; Jiuchun Jiang. Simplified Battery Pack Modeling Considering Inconsistency and Evolution of Current Distribution. IEEE Transactions on Intelligent Transportation Systems 2020, 22, 630 -639.

AMA Style

Xinyuan Fan, Weige Zhang, Zhanguo Wang, Fulai An, Hao Li, Jiuchun Jiang. Simplified Battery Pack Modeling Considering Inconsistency and Evolution of Current Distribution. IEEE Transactions on Intelligent Transportation Systems. 2020; 22 (1):630-639.

Chicago/Turabian Style

Xinyuan Fan; Weige Zhang; Zhanguo Wang; Fulai An; Hao Li; Jiuchun Jiang. 2020. "Simplified Battery Pack Modeling Considering Inconsistency and Evolution of Current Distribution." IEEE Transactions on Intelligent Transportation Systems 22, no. 1: 630-639.

Journal article
Published: 04 March 2020 in IEEE Access
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An improved method for the remaining useful life (RUL) prognostic of Lithium-ion batteries with Li(NiMnCo)O2 cathode using improved unscented particle filter (UPF) is proposed with respect to capacity diving in later capacity degradation curve. Key points of this paper are: (1) An appropriate empirical model for the situation as the most contributive work, is put forward as an alternative to the widely used UPF models, and the prediction performance is respectively verified by least square fitting and the improved UPF; (2) Systematic noise in Gamma distribution is attempted in state space equations of the proposed method, so as to avoid potential shape shifting of the prediction curve after sampling the particles with Gaussian noise, for model parameters could get zero-crossed; (3) With training data preprocessed considering the capacity recovery phenomenon concisely, the residual error and root mean square error of fitting could get further reduced, as a supplement to traditional treatments like smoothing, thus relieving the sensitivity of data-driven methods to data by enhancing quality. Validations are implemented by applying the proposed method to the battery data by conducting cycle aging tests under different working conditions, where improved approximation and prediction performance can be obtained.

ACS Style

Xinwei Cong; Caiping Zhang; Jiuchun Jiang; Weige Zhang; Yan Jiang; Xinyu Jia. An Improved Unscented Particle Filter Method for Remaining Useful Life Prognostic of Lithium-ion Batteries With Li(NiMnCo)O2 Cathode With Capacity Diving. IEEE Access 2020, 8, 58717 -58729.

AMA Style

Xinwei Cong, Caiping Zhang, Jiuchun Jiang, Weige Zhang, Yan Jiang, Xinyu Jia. An Improved Unscented Particle Filter Method for Remaining Useful Life Prognostic of Lithium-ion Batteries With Li(NiMnCo)O2 Cathode With Capacity Diving. IEEE Access. 2020; 8 (99):58717-58729.

Chicago/Turabian Style

Xinwei Cong; Caiping Zhang; Jiuchun Jiang; Weige Zhang; Yan Jiang; Xinyu Jia. 2020. "An Improved Unscented Particle Filter Method for Remaining Useful Life Prognostic of Lithium-ion Batteries With Li(NiMnCo)O2 Cathode With Capacity Diving." IEEE Access 8, no. 99: 58717-58729.

Journal article
Published: 03 March 2020 in Journal of Cleaner Production
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In the process of electric vehicle marketization, the charging infrastructure, especially the planning and construction of fast charging stations, is extremely important. The construction of charging stations is limited by various social factors and should adapt to the development of the charging demand. Therefore, based on the background research that has considered the social factors and determined a certain number of candidate sites, this paper proposes a method that selects the optimal constructing plan of charging stations with the lowest social costs from the candidate construction plans. First, an optimization model for siting and sizing the charging stations is created and then, the optimal number and location of the charging stations in the unplanned areas and the optimal number and power of in-station charging facilities are obtained by solving the model. After that, two development trends of the charging demand are considered. In case the existing charging stations are not suitable for the changed charging demand, the charging station construction scale is expanded or downsized based on the sequential strategy of the charging station construction, which helps avoid the fund loss and resource waste.

ACS Style

Xuyao Meng; Weige Zhang; Yan Bao; Yian Yan; Ruiming Yuan; Zhen Chen; Jingxin Li. Sequential construction planning of electric taxi charging stations considering the development of charging demand. Journal of Cleaner Production 2020, 259, 120794 .

AMA Style

Xuyao Meng, Weige Zhang, Yan Bao, Yian Yan, Ruiming Yuan, Zhen Chen, Jingxin Li. Sequential construction planning of electric taxi charging stations considering the development of charging demand. Journal of Cleaner Production. 2020; 259 ():120794.

Chicago/Turabian Style

Xuyao Meng; Weige Zhang; Yan Bao; Yian Yan; Ruiming Yuan; Zhen Chen; Jingxin Li. 2020. "Sequential construction planning of electric taxi charging stations considering the development of charging demand." Journal of Cleaner Production 259, no. : 120794.

Journal article
Published: 20 January 2020 in Energies
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With the rapid growth of renewable energy and the DC fast charge pile of the electric vehicle, their inherent volatility and randomness increase a power system’s unbalance of instantaneous power. The need for power grid frequency regulation is increasing. The energy storage system (ESS) can be used to assist the thermal power unit so that a better frequency regulation result is obtained without changing the original operating mode of the unit. In this paper, a set of different charging/discharging control strategies of the lithium titanate battery (LTO) is proposed, which are chosen according to the interval of the State of energy (SOE) to improve the utilization rate of the ESS. Finally, the cost-benefit model of the ESS participating in automatic generation control ancillary service is established. Case analysis proves that after a 1.75 MWh ESS is configured for a 600 MW thermal power unit, Kp and D is increased from 1.42 to 6.38 and 2857 to 6895 MW. The net daily income is increased from 20,284 yuan to 199,900 yuan with a repayment period of 93 days. The results show that the control strategies and the energy configuration method can improve the performance and economic return of the system.

ACS Style

Bingxiang Sun; Xitian He; Weige Zhang; Yangxi Li; Minming Gong; Yang Yang; Xiaojia Su; Zhenlin Zhu; Wenzhong Gao. Control Strategies and Economic Analysis of an LTO Battery Energy Storage System for AGC Ancillary Service. Energies 2020, 13, 505 .

AMA Style

Bingxiang Sun, Xitian He, Weige Zhang, Yangxi Li, Minming Gong, Yang Yang, Xiaojia Su, Zhenlin Zhu, Wenzhong Gao. Control Strategies and Economic Analysis of an LTO Battery Energy Storage System for AGC Ancillary Service. Energies. 2020; 13 (2):505.

Chicago/Turabian Style

Bingxiang Sun; Xitian He; Weige Zhang; Yangxi Li; Minming Gong; Yang Yang; Xiaojia Su; Zhenlin Zhu; Wenzhong Gao. 2020. "Control Strategies and Economic Analysis of an LTO Battery Energy Storage System for AGC Ancillary Service." Energies 13, no. 2: 505.

Journal article
Published: 10 January 2020 in IEEE Transactions on Vehicular Technology
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Catenary-free trams powered by on-board supercapacitor systems require high charging power from tram stations along the line. Since a shared electric grid is suffering from power superimposition when several trams charge at the same time, we propose to install stationary energy storage systems (SESSs) for power supply network to downsize charging equipment and reduce operational cost of the electric grid. To evaluate the trade-off between component cost and operational cost, an optimisation problem, which integrates type selection, sizing, energy management and different installation configurations of the SESSs, is introduced and formulated in terms of an annual cost. Disciplined convex modelling is applied to obtain a computationally tractable solution for a case study on an existing line in China. Results show that the optimal solution may reduce tramline cost by 11.48%.

ACS Style

Shaoyuan Wei; Jiuchun Jiang; Nikolce Murgovski; Jonas Sjoberg; Weige Zhang; Caiping Zhang; Xiaosong Hu. Optimisation of a Catenary-Free Tramline Equipped With Stationary Energy Storage Systems. IEEE Transactions on Vehicular Technology 2020, 69, 2449 -2462.

AMA Style

Shaoyuan Wei, Jiuchun Jiang, Nikolce Murgovski, Jonas Sjoberg, Weige Zhang, Caiping Zhang, Xiaosong Hu. Optimisation of a Catenary-Free Tramline Equipped With Stationary Energy Storage Systems. IEEE Transactions on Vehicular Technology. 2020; 69 (3):2449-2462.

Chicago/Turabian Style

Shaoyuan Wei; Jiuchun Jiang; Nikolce Murgovski; Jonas Sjoberg; Weige Zhang; Caiping Zhang; Xiaosong Hu. 2020. "Optimisation of a Catenary-Free Tramline Equipped With Stationary Energy Storage Systems." IEEE Transactions on Vehicular Technology 69, no. 3: 2449-2462.

Journal article
Published: 13 December 2019 in IEEE Access
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Nowadays, the lithium-ion battery (LIB) has been widely used as an energy source for electric vehicles or the auxiliary power supply for rail transit. However, the sensitivity of LIB to temperature greatly limits its application condition. Due to internal impedance and entropy change, temperature of LIB varies during charging and discharging. This variation conversely affects the electrical characteristics of batteries, and then the heat generation rate. In order to accurately model this coupled thermoelectric process of the lithium-ion battery, a co-simulation method based on coupled thermoelectric model is developed in this paper, which combines equivalent circuit model (ECM) and Computational Fluid Dynamics (CFD) software. For purpose of verifying the feasibility and accuracy of the proposed method, Experiments are performed at different temperatures and C-rates conditions, using a large-format pouch cell applied in rail transit projects. The comparison indicates that this method joined with temperature correction has higher accuracy than the traditional thermal simulation without temperature correction and the simulated result shows good agreement with measurements.

ACS Style

Weinan Huang; Weige Zhang; Anci Chen; Yanru Zhang; Ming Li. A Co-Simulation Method Based on Coupled Thermoelectric Model for Electrical and Thermal Behavior of the Lithium-ion Battery. IEEE Access 2019, 7, 180727 -180737.

AMA Style

Weinan Huang, Weige Zhang, Anci Chen, Yanru Zhang, Ming Li. A Co-Simulation Method Based on Coupled Thermoelectric Model for Electrical and Thermal Behavior of the Lithium-ion Battery. IEEE Access. 2019; 7 (99):180727-180737.

Chicago/Turabian Style

Weinan Huang; Weige Zhang; Anci Chen; Yanru Zhang; Ming Li. 2019. "A Co-Simulation Method Based on Coupled Thermoelectric Model for Electrical and Thermal Behavior of the Lithium-ion Battery." IEEE Access 7, no. 99: 180727-180737.

Journal article
Published: 28 November 2019 in IEEE Transactions on Industrial Electronics
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Lithium titanate batteries with Li4Ti5O12 anodes, which show excellent power characteristics and cycle life, are promising candidates for electric vehicle applications. However, the conventional equivalent circuit model becomes insufficient when the temperature and current rate range widely. In this paper, a novel battery model is established by re-deriving and simplifying the Bulter-Volmer equation carefully, and then embedding it in the equivalent circuit model along with the Nernst equation to adapt the model to the variation of the temperature and current rate. Furthermore, the non-Arrhenius behavior of the ohmic resistance is considered in this paper and corrected by a quadratic polynomial method. The proposed model is thoroughly validated at different temperatures with both constant current tests and dynamic current tests, and the verification results show satisfying accuracy. Through the comparison with the conventional equivalent circuit model, significant improvement on model performance is exhibited.

ACS Style

Anci Chen; Weige Zhang; Caiping Zhang; Weinan Huang; Sijia Liu. A Temperature and Current Rate Adaptive Model for High-Power Lithium-Titanate Batteries Used in Electric Vehicles. IEEE Transactions on Industrial Electronics 2019, 67, 9492 -9502.

AMA Style

Anci Chen, Weige Zhang, Caiping Zhang, Weinan Huang, Sijia Liu. A Temperature and Current Rate Adaptive Model for High-Power Lithium-Titanate Batteries Used in Electric Vehicles. IEEE Transactions on Industrial Electronics. 2019; 67 (11):9492-9502.

Chicago/Turabian Style

Anci Chen; Weige Zhang; Caiping Zhang; Weinan Huang; Sijia Liu. 2019. "A Temperature and Current Rate Adaptive Model for High-Power Lithium-Titanate Batteries Used in Electric Vehicles." IEEE Transactions on Industrial Electronics 67, no. 11: 9492-9502.

Journal article
Published: 28 November 2019 in IEEE Access
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The existence of the consistency degradation of the battery pack hinders the accurate estimation of pack capacity and cell capacity in the battery pack. The paper focuses on the capacity estimation of cells in the serial battery pack. The shape invariance of the charging voltage curve is discussed and used as the theoretical foundation of cell capacity difference identification. The matching relationship between two voltage curves is obtained based on the dynamic time warping algorithm. Then the capacity difference identification algorithm to calculate the capacity difference between the two cells is proposed. Based on the algorithm, a three-step capacity estimation method is established. The proposed method can only use the previous charging curve of one cell in the pack and the current charging data of the battery pack to rapidly estimate the capacity of each cell in the battery pack. A 16 serial LiFePO4 battery pack is employed to verify the method. The result shows the estimation error of cell capacities is less than 3% rated capacity. With this method, the cell capacities in the pack can be rapidly and accurately estimated, providing a foundation for the consistency analysis and equalization of the battery pack.

ACS Style

Yang Liu; Caiping Zhang; Jiuchun Jiang; Yan Jiang; Linjing Zhang; Weige Zhang. Capacity Estimation of Serial Lithium-ion Battery Pack Using Dynamic Time Warping Algorithm. IEEE Access 2019, 7, 174687 -174698.

AMA Style

Yang Liu, Caiping Zhang, Jiuchun Jiang, Yan Jiang, Linjing Zhang, Weige Zhang. Capacity Estimation of Serial Lithium-ion Battery Pack Using Dynamic Time Warping Algorithm. IEEE Access. 2019; 7 (99):174687-174698.

Chicago/Turabian Style

Yang Liu; Caiping Zhang; Jiuchun Jiang; Yan Jiang; Linjing Zhang; Weige Zhang. 2019. "Capacity Estimation of Serial Lithium-ion Battery Pack Using Dynamic Time Warping Algorithm." IEEE Access 7, no. 99: 174687-174698.

Journal article
Published: 03 March 2019 in Sustainability
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The power fluctuations of grid-connected photovoltaic (PV) systems have negative impacts on the power quality and stability of the utility grid. In this study, the combinations of a battery/supercapacitor hybrid energy storage system (HESS) and the PV power curtailment are used to smooth PV power fluctuations. A PV power curtailment algorithm is developed to limit PV power when power fluctuation exceeds the power capacity of the HESS. A multi-objective optimization model is established to dispatch the HESS power, considering energy losses and the state of charge (SOC) of the supercapacitor. To prevent the SOCs of the HESS from approaching their lower limits, a SOC correction strategy is proposed to correct the SOCs of the HESS. Moreover, this paper also investigates the performances (such as the smoothing effects, losses and lifetime of energy storage, and system net profits) of two different smoothing strategies, including the method of using the HESS and the proposed strategy. Finally, numerous simulations are carried out based on data obtained from a 750 kWp PV plant. Simulation results indicate that the proposed method is more economical and can effectively smooth power fluctuations compared with the method of using the HESS.

ACS Style

Wei Ma; Wei Wang; Xuezhi Wu; Ruonan Hu; Fen Tang; Weige Zhang. Control Strategy of a Hybrid Energy Storage System to Smooth Photovoltaic Power Fluctuations Considering Photovoltaic Output Power Curtailment. Sustainability 2019, 11, 1324 .

AMA Style

Wei Ma, Wei Wang, Xuezhi Wu, Ruonan Hu, Fen Tang, Weige Zhang. Control Strategy of a Hybrid Energy Storage System to Smooth Photovoltaic Power Fluctuations Considering Photovoltaic Output Power Curtailment. Sustainability. 2019; 11 (5):1324.

Chicago/Turabian Style

Wei Ma; Wei Wang; Xuezhi Wu; Ruonan Hu; Fen Tang; Weige Zhang. 2019. "Control Strategy of a Hybrid Energy Storage System to Smooth Photovoltaic Power Fluctuations Considering Photovoltaic Output Power Curtailment." Sustainability 11, no. 5: 1324.

Journal article
Published: 12 February 2019 in Energies
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With the pervasiveness of electric vehicles and an increased demand for fast charging, stationary high-power fast-charging is becoming more widespread, especially for the purpose of serving pure electric buses (PEBs) with large-capacity onboard batteries. This has resulted in a huge distribution capacity demand. However, the distribution capacity is limited, and in some urban areas the cost of expanding the electric network capacity is very high. In this paper, three battery energy storage system (BESS) integration methods—the AC bus, each charging pile, or DC bus—are considered for the suppression of the distribution capacity demand according to the proposed charging topologies of a PEB fast-charging station. On the basis of linear programming theory, an evaluation model was established that consider the influencing factors of the configuration: basic electricity fee, electricity cost, cost of the energy storage system, costs of transformer and converter equipment, and electric energy loss. Then, a case simulation is presented using realistic operation data, and an economic comparison of the three configurations is provided. An analysis of the impacts of each influence factor in the case study is discussed to verify the case results. The numerical results indicate that the appropriate BESS configuration can significantly reduce the distribution demand and stationary cost synchronously.

ACS Style

Yian Yan; Huang Wang; Jiuchun Jiang; Weige Zhang; Yan Bao; Mei Huang. Research on Configuration Methods of Battery Energy Storage System for Pure Electric Bus Fast Charging Station. Energies 2019, 12, 558 .

AMA Style

Yian Yan, Huang Wang, Jiuchun Jiang, Weige Zhang, Yan Bao, Mei Huang. Research on Configuration Methods of Battery Energy Storage System for Pure Electric Bus Fast Charging Station. Energies. 2019; 12 (3):558.

Chicago/Turabian Style

Yian Yan; Huang Wang; Jiuchun Jiang; Weige Zhang; Yan Bao; Mei Huang. 2019. "Research on Configuration Methods of Battery Energy Storage System for Pure Electric Bus Fast Charging Station." Energies 12, no. 3: 558.

Journal article
Published: 24 July 2018 in Applied Sciences
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In order to reduce the recharging time of electric vehicles, the charging power and voltage are becoming higher, which has led to a huge distribution capacity demand and load fluctuation, especially in pure electric buses (PEBs) with large onboard batteries. Based on one actual direct current (DC) fast-charging station, a two-step strategy for the suppression of the peak charging power was developed in this paper, which combined charging optimization and a battery energy storage system (BESS) configuration. A novel charging strategy was proposed, with the PEBs fast-charging during operating hours and normal charging at night, based on a new charging topology. Then, a charging sequence optimization model was established, according to the operation characteristics analysis of the DC fast-charging station. The particle swarm optimization (PSO) algorithm is applied to optimize the charging sequence, which is disordered at present. Linear programming is used to configure the battery energy storage system in order to further decrease the peak charging power and satisfy the distribution capacity constraint. The two-step strategy was simulated by the dataset from the real station. The results show that the distribution capacity demand, charging load fluctuation, electricity cost, and size of the BESS were significantly decreased.

ACS Style

Yian Yan; Jiuchun Jiang; Weige Zhang; Mei Huang; Qiang Chen; Huang Wang. Research on Power Demand Suppression Based on Charging Optimization and BESS Configuration for Fast-Charging Stations in Beijing. Applied Sciences 2018, 8, 1212 .

AMA Style

Yian Yan, Jiuchun Jiang, Weige Zhang, Mei Huang, Qiang Chen, Huang Wang. Research on Power Demand Suppression Based on Charging Optimization and BESS Configuration for Fast-Charging Stations in Beijing. Applied Sciences. 2018; 8 (8):1212.

Chicago/Turabian Style

Yian Yan; Jiuchun Jiang; Weige Zhang; Mei Huang; Qiang Chen; Huang Wang. 2018. "Research on Power Demand Suppression Based on Charging Optimization and BESS Configuration for Fast-Charging Stations in Beijing." Applied Sciences 8, no. 8: 1212.